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Cloud Based Big Data Application of FP-Growth Algorithm and K-Means Clustering Algorithm Based on MapReduce Hadoop Than
| Content Provider | Semantic Scholar |
|---|---|
| Author | Aung, Htike Htike |
| Copyright Year | 2018 |
| Abstract | In current time large volumes of data are being produced by various modern applications at an ever increasing rate. These applications range from wireless sensors networks to social networks. The automatic analysis of such huge data volume is a challenging task since a large amount of interesting knowledge can be extracted. Association rule mining is an exploratory data analysis method able to discover interesting and hidden correlations among data. Since this data mining process is characterized by computationally intensive tasks, efficient distributed approaches are needed to increase its scalability. This paper proposes a cloud-based service, named parallel FP-growth, to efficiently mine association rules on a distributed computing model. It consists of a series of distributed MapReduce jobs run in the cloud. Each job performs a different step in the association rule mining process, followed by cloud-based parallel k-means clustering algorithm to produce similar groups These outputs are verify and filter by three conditional levels which results is useful rules. As a case study, the proposed approach has been applied to the educational data scenario. Keywordsassociation rule mining, distributed computing model, cloud-based service, network data analysis. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://uit.edu.mm/2017_ICAIT/2017-003.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
